arxiv_cv 90% Match Research Paper Robotics engineers,AR/VR developers,Researchers in 3D computer vision,Computer graphics professionals 3 weeks ago

Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration

computer-vision › 3d-vision

📄 Abstract

Abstract: With the rise in consumer depth cameras, a wealth of unlabeled RGB-D data has become available. This prompts the question of how to utilize this data for geometric reasoning of scenes. While many RGB-D registration meth- ods rely on geometric and feature-based similarity, we take a different approach. We use cycle-consistent keypoints as salient points to enforce spatial coherence constraints during matching, improving correspondence accuracy. Additionally, we introduce a novel pose block that combines a GRU recurrent unit with transformation synchronization, blending historical and multi-view data. Our approach surpasses previous self- supervised registration methods on ScanNet and 3DMatch, even outperforming some older supervised methods. We also integrate our components into existing methods, showing their effectiveness.

Authors (6)

Siddharth Tourani

Jayaram Reddy

Sarvesh Thakur

K Madhava Krishna

Muhammad Haris Khan

N Dinesh Reddy

Submitted

October 16, 2025

arXiv Category

cs.CV

2024 IEEE International Conference on Robotics and Automation (ICRA)

arXiv PDF

Key Contributions

This paper proposes a self-supervised RGB-D registration method using cycle-consistent keypoints to enforce spatial coherence and a novel pose block combining GRU with transformation synchronization. This approach effectively utilizes unlabeled RGB-D data to improve correspondence accuracy and pose estimation, outperforming previous self-supervised methods.

Business Value

Enables more robust and cost-effective 3D mapping and scene reconstruction for robotics and AR/VR applications by reducing the need for manual labeling or expensive sensors.

Paper Metadata

Innovation Type

Novel Self-Supervised Approach

Deployment Feasibility

Moderate. Requires RGB-D sensors and computational resources for processing, but the self-supervised nature reduces data acquisition costs.

Limitations Addressed

Reliance of existing methods on geometric/feature similarity,Need for large amounts of labeled data for supervised registration,Challenges in achieving accurate correspondences with sparse data

Performance Gains

Surpassed previous self-supervised registration methods and even some older supervised methods on benchmark datasets.

Technical Tags

RGB-D registrationself-supervised learningcycle-consistent keypointsspatial coherencepose estimationGRUtransformation synchronizationunlabeled datageometric reasoning

Research Topics

3D Computer VisionGeometric RegistrationSelf-Supervised LearningRoboticsScene Understanding

Methods & Architectures

Cycle-consistent keypointsGRU recurrent unitTransformation synchronizationPose blockSelf-supervised learning GRU (Gated Recurrent Unit)

Applications & Tasks

Robotics Augmented Reality (AR) Virtual Reality (VR) 3D Mapping Autonomous Driving Accurate RGB-D RegistrationLeveraging Unlabeled DataImproving Correspondence Accuracy 3D Scene RegistrationPose EstimationPoint Cloud Alignment

Datasets & Benchmarks

Datasets

ScanNet, 3DMatch

Benchmarks

Outperforms previous self-supervised methods on ScanNet and 3DMatch • Outperforms some older supervised methods on ScanNet and 3DMatch

Registration accuracyPose estimation error

Related Fields

RoboticsComputer GraphicsMachine Learning3D VisionSensor Fusion

Keywords

RGB-D registrationself-supervised learningkeypointspose estimation3D reconstructionpoint cloud alignmentgeometric reasoningunlabeled dataroboticsARVRScanNet3DMatch

Academic Context

#3D Computer Vision#Geometric Registration#Self-Supervised Learning#Robotics#Scene Understanding

Technology Stack

Frameworks & Libraries

PyTorch

Programming Languages

Python

Commercial Potential

Potential Products

3D mapping software for robotsAR/VR scene reconstruction toolsPose estimation modules for autonomous systems

Target Industries

RoboticsGamingVirtual RealityAugmented RealityAutonomous Vehicles

Use Case Examples

Enabling robots to build accurate 3D maps of their environmentCreating immersive AR experiences by aligning virtual objects with the real worldImproving SLAM systems

Competitive Edge

Offers a novel self-supervised approach that leverages cycle consistency and recurrent networks for robust RGB-D registration, achieving state-of-the-art performance without requiring labeled data.

Market Opportunity

Growing markets for robotics, AR, and VR.

Revenue Models

Licensing of algorithmsintegration into robotics platforms.

Resource Requirements

Compute Needs

Moderate to high, depending on the size of the point clouds and the complexity of the scene.

Data Requirements

Unlabeled RGB-D data from various environments.

Deployment Constraints

Requires RGB-D sensors,Computational resources for real-time processing

Scalability

Scalability to large-scale environments requires efficient memory management and processing.

Production Readiness

Maturity Level

Research

Time to Market

2-3 years

Patent Potential

Moderate, for the cycle-consistent keypoint approach and the pose block.

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